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Enterprise application integration task-based execution model experimental dataset.

Rafael Z Frantz1, Raquel M Pillat2, Fabricia Roos-Frantz1

  • 1Unijuí University, Ijuí/RS, Brazil.

Data in Brief
|April 18, 2022
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Summary

This data article presents log files from computational experiments on enterprise application integration platforms. The data aids in analyzing the task-based execution model

Keywords:
Enterprise application integrationIntegration frameworkIntegration platformMakespanPerformance analysisWorkflow execution

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Area of Science:

  • Computer Science
  • Software Engineering

Background:

  • Enterprise application integration (EAI) platforms are crucial for modern business operations.
  • The task-based execution model offers a structured approach to managing complex integration processes.
  • Understanding the performance characteristics of this model is essential for optimizing EAI systems.

Purpose of the Study:

  • To provide a raw dataset of log files from automated computational experiments.
  • To support research on the makespan and performance of the task-based execution model in EAI.
  • To enable analysis and comparison of different execution models and platforms.

Main Methods:

  • Raw log files were captured during automated computational experiments.
  • The code of a standard enterprise application integration process was modified for data recording.
  • The dataset includes CSV files detailing experiment executions, makespan, message counts, and work-units processed.

Main Results:

  • The dataset contains detailed performance metrics from EAI experiments.
  • Log files capture makespan, message throughput, and work-unit processing.
  • The data is directly related to empirical studies on the task-based execution model.

Conclusions:

  • This dataset offers valuable resources for the scientific community studying workflow and integration processes.
  • Researchers can use this data to analyze the behavior of the task-based execution model.
  • The data facilitates gaining insights for improving EAI platforms and comparing execution models.